Experimental results, reported in Table REF , show that the proposed approach enables to effectively train the Student network in a fully-unsupervised way, reaching 83.77% IoU on the EndoVis2017VOS dataset, 12.30% above the unsupervised AGSD approach and only 5.22% below the fully-supervised baseline. As hypothesized, the noise affecting the pseudo-labels generated by optical-flow segmentation cannot be predicted from the individual frames, thus cannot be learnt by the Proxy network. This results in a significant improvement of the Proxy network's predictions compared to pseudo-labels used for its training (+34.70% \(\Delta \) IoU on EndoVis2017VOS). On top of this, the Student network significantly improves the segmentation quality, by training only on the probably well-labelled regions of the pseudo-labels, selected by means of the local IoU between pseudo-labels and Proxy predictions: the improvement of the Student network, with respect to the Proxy network, amounts to +8.99% \(\Delta \) IoU on EndoVis2017VOS. Qualitative results presented in Figure REF  clearly show the dramatic improvement of the Proxy network compared to the Teacher network, and the refining effect of the Student network, producing accurate and sharp segmentation masks. In order to assess statistical significance of the results on the EndoVis2017VOS dataset, pairwise t-tests were run (sample size N=1800) between Proxy & Teacher, Student & Proxy and baselineFS & Student, all showing statistically significant differences (\(p << 0.001\)  for all the three pairs). In addition, Cohen's d number was computed for such pairs, in order to quantify the strength of such statistically significant difference. Cohen's d numbers analysis, reported in Table REF , shows that the effect-size of such differences is very large between Proxy & Teacher (\(d > 1.2\) , d = 1.566), medium/high between Student & Proxy (\(0.5 < d < 0.8\) , d = 0.612) and medium/small between fully-supervised baseline & Student (\(0.2 < d < 0.5\) , d = 0.448) (according to [1]}, [2]}). As expected, the performance on the EndoVis2017Challenge dataset, where devices such as the Ultra-Sound probe are considered as part of the background class, is lower than the one on EndoVis2017VOS, while still outperforming the unsupervised AGSD approach (+8.40% \(\Delta \) IoU). This is due to the fact that our approach, despite not being trained using specific shape-priors of these tools, is still able to generalize and segment them together with the da Vinci ones. Examples of frames containing the drop-in Ultra-Sound probe are shown in Figure REF , first and fourth row from the top. In order for our approach to learn such semantic discrimination between the two instrument classes, pure motion information may not be sufficient. The possible extension of FUN-SIS to multi-class segmentation will be discussed in Section . We also analyze the difference between the 2-step and 3-step training strategies described in Section REF . Results, shown in Figure REF , confirm that the two modalities provide comparable results, as suggested in Section REF . We thus consider the 2-step approach superior, due to the shorter training time required. Results obtained on the challenging STRAS dataset, reported in Table REF , confirm the ability of the method to effectively learn surgical tool segmentation in a fully-unsupervised way. The Student network, trained without any domain-specific hyper-parameter tuning, reaches an IoU equal to 66.37%, despite being trained on very low-quality pseudo-labels (29.93% IoU). As observable from Figure REF , in fact, optical-flow images appear less sharp compared to the EndoVis2017 ones, mainly due to image blurring and lower image resolution, influencing the overall performance. The implications of the method's dependency on optical-flow quality will be discussed in Section . Additional qualitative results for the Student network on the EndoVis2017VOS and STRAS datasets are displayed in Figures REF &REF , at the end of the manuscript.
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